[Doc] Group examples into categories (#11782)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

examples/fp8/README.md

@@ -56,7 +56,7 @@ python3 examples/fp8/extract_scales.py --quantized_model <QUANTIZED_MODEL_DIR> -
 ```
 ### 4. Load KV Cache Scaling Factors into VLLM.
 This script evaluates the inference throughput of language models using various backends such as vLLM. It measures the time taken to process a given number of prompts and generate sequences for each prompt. The recently generated KV cache scaling factors are now integrated into the benchmarking process and allow for KV cache scaling factors to be utilized for FP8.
-```python
+```
 # prerequisites:
 # -  LLaMa 2 kv_cache_scales.json file
 
@@ -90,7 +90,7 @@ optional arguments:
   --kv-cache-dtype {auto,fp8} Data type for kv cache storage. If "auto", will use model data type. FP8_E5M2 (without scaling) is only supported on cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported ```for common inference criteria.
   --quantization-param-path QUANT_PARAM_JSON Path to the JSON file containing the KV cache scaling factors. This should generally be supplied, when KV cache dtype is FP8. Otherwise, KV cache scaling factors default to 1.0, which may cause accuracy issues. FP8_E5M2 (without scaling) is only supported on cuda version greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported for common inference criteria.
 ```
-```
 Example:
+```console
 python3 benchmarks/benchmark_throughput.py --input-len <INPUT_LEN> --output-len <OUTPUT_LEN> -tp <TENSOR_PARALLEL_SIZE> --kv-cache-dtype fp8 --quantization-param-path <path/to/kv_cache_scales.json> --model <path-to-llama2>
-```python
+```